Main navigation

Data In Crime Isn't All That It Seems

Article views

3718

VIEWS

The use of data in fighting crime is well established, it is commonly known that police forces across the world have utilized it to strategically place their units.

Through real-time data modelling it is possible for them to accurately predict where crimes are likely to take place and what the crimes are likely to be. Through this modelling, action can be taken to help prevent crime, from extra patrols on the street, to making investments in locks and other anti-theft devices.

Many police forces across the globe have been trialling this, but does it actually work?

There have been mixed reviews from it, with some areas, such as Trafford in Manchester which saw a 26.6% decrease in burglaries during 2011, through to the police force in Kent who had a successful limited trial and then saw crime increase when it was rolled out across the entire county.

Generally speaking there are more examples of forces using it effectively, but it is not a universal success. Why is this the case though?

Inaccurate Data

One of the key reasons for a failing in any algorithm is that the data being input is not good enough. If crime is over or under reported, then the quality of the overall accuracy is going to be poor.

For instance, if a crime is recorded every time the emergency services are called, then a particularly conservative area may appear to have a higher crime level when the reality is quite different. Imagine if you live in a high crime area and saw somebody selling drugs, the chances are that most people would not report it because it is a daily occurrence. In a low crime area it may appear that there is a drug epidemic purely because this person is an anomaly and therefore far more likely to be reported.

This can lead to resources being placed in the wrong places and taken away from areas which may benefit from them more.

Not Looking At Social Issues

It is a well established fact that areas that experience significant social issues experience higher crime rates, for instance, a lack of jobs creates poverty which in turn creates more crime.

The analytical approach could be seen as a cure that fights the symptoms without tackling the disease. It means that regardless of if they stop crime in one area, desperate people are likely to turn to more drastic measures to survive. This could either be more serious crimes to increase the payoff or simply doing what they already do, but in a different area.

In terms of decreasing crime in an area, making the community better off is going to be the best way to start and this is something that analytical policing simply does not focus on.

Victimization

One of the key elements of having a policing policy revolving around data is that it places police in areas with high crime rates, which can cause feelings of victimization.

If somebody in a particular area experiences being stopped and searched frequently, not only will they become frustrated, but it is likely that they will turn against authorities and either become a greater risk of committing crime or not reporting it when they are victims themselves.

Although unproven due to a lack of data, there are considerable numbers of case studies that suggest the more disconnect that people feel with the police, the more likely they are to commit crime. Through essentially creating an area where police are more likely to stop and search somebody, that area is likely to experience more disconnect and therefore have an increased likelihood of crime.